EDA-1
Andriy Koval
May 7, 2020
Abstract
The reports looks at progression of COVID-19 within OECD countries and the overall magnitude of governments’ response to the pandemic.
Environment
library(ggplot2) #For graphing
library(magrittr) #Pipes
library(dplyr) # for shorter function names. but still prefer dplyr:: stems
library(knitr) # dynamic documents
library(rmarkdown) # dynamic
library(kableExtra) # enhanced tables, see http://haozhu233.github.io/kableExtra/awesome_table_in_html.html
# library(TabularManifest) # exploratory data analysis, see https://github.com/Melinae/TabularManifest
requireNamespace("knitr", quietly=TRUE)
requireNamespace("scales", quietly=TRUE) #For formating values in graphs
requireNamespace("RColorBrewer", quietly=TRUE)
requireNamespace("dplyr", quietly=TRUE)
requireNamespace("DT", quietly=TRUE) # for dynamic tables
# requireNamespace("plyr", quietly=TRUE)
# requireNamespace("reshape2", quietly=TRUE) #For converting wide to long
# requireNamespace("mgcv, quietly=TRUE) #For the Generalized Additive Model that smooths the longitudinal graphs.config <- config::get()
source("./scripts/common-functions.R") # reporting functions and quick views
# source("./scripts/graphing/graph-presets.R") # font and color conventions
# source("./scripts/graphing/graph-support.R") # font and color conventionsggplot2::theme_set(
ggplot2::theme_bw(
)+
theme(
strip.background = element_rect(fill="grey90", color = NA)
)
)compute_epi_timeline <- function(d, n_deaths_first_day = 1) { #}, d_country ){
# browser()
# d <- ds_cgrt %>%
# # filter(country_code %in% c("ITA","FRA") ) %>%
# filter(country_code %in% c("AFG") ) %>%
# select(country_code, date, n_cases, n_deaths)
#
d_out <- d %>%
# dplyr::filter(country_code %in% unique(d_country$id)) %>%
dplyr::group_by(country_code) %>%
dplyr::mutate(
# this solution might be vulnerable to cases where some intermediate dates are missed
n_deaths_cum = cumsum(tidyr::replace_na(n_deaths,0))
,n_cases_cum = cumsum(tidyr::replace_na(n_cases,0))
,cutoff_death = n_deaths_cum >= 1
,cutoff_case = n_cases_cum >= 1
,days_since_1death = cumsum(tidyr::replace_na(cutoff_death,0))
,days_since_1case = cumsum(tidyr::replace_na(cutoff_case,0))
,date_of_1death = lubridate::as_date(ifelse(days_since_1death==1,date, NA))
,date_of_1case = lubridate::as_date(ifelse(days_since_1case==1,date, NA))
,date_of_1death = min(date_of_1death, na.rm =T)
,date_of_1case = min(date_of_1case, na.rm =T)
,days_since_1death = date - date_of_1death
,days_since_1case = date - date_of_1case
,n_deaths_cum_per_1m = n_deaths_cum/n_population_2018*1000000
,n_cases_cum_per_1m = n_cases_cum/ n_population_2018*1000000
) %>%
dplyr::ungroup() %>%
# dplyr::filter(epi_timeline > 0) %>%
dplyr::mutate(
days_since_exodus = as.integer(date - lubridate::date("2020-01-13")) # first case outside of china
,days_since_pandemic = as.integer(date - lubridate::date("2020-03-11")) # WHO declares pandemic
) %>%
select(-cutoff_death, - cutoff_case, -date_of_1death, -date_of_1case)
return(d_out)
}
# for testing the function:
# d_out <- ds0 %>% filter(country_code == "ITA") %>%
# select(
# country_code, date,n_cases, n_deaths, ConfirmedDeaths, ConfirmedCases
# ) %>%
# compute_epi_timeline()
margings_for_plotly <- list(
l = 50,
r = 50,
b = 100,
t = 100,
pad = 4
)Data Sources
- Cases and mortlaity for COVID-19 are sourced from the European Centre for Disease Prevention and Control
- Government Response metric to COVID-19 (Stringency Index) is sources from Oxford COVID-19 Government Response Tracker (OxCGRT)
# reference table for geographic units (see ./manipulation/ellis-geography.R)
ds_geo <- readr::read_csv("./data-public/metadata/world-geography.csv")
# ds_geo %>% glimpse()
# COVID spread and mortality (see ./manipulation/ellis-covid.R)
ds_covid <- readr::read_csv(config$path_input_covid)
# ds_covid %>% glimpse()
# OxCGRT - COVID Government Response Tracker (see ./manipulation/ellis-cgrt.R)
ds_cgrt <- readr::read_rds("./data-unshared/derived/OxCGRT.rds")
# to keep it manageble during exploration
ds_cgrt <- ds_cgrt %>% select(country_code, date, StringencyIndex,GovernmentResponseIndex,ContainmentHealthIndex,EconomicSupportIndex )
# ds_cgrt %>% glimpse()ds0 <- ds_covid %>%
compute_epi_timeline() %>%
dplyr::left_join(
ds_cgrt
,by = c("date", "country_code")
) %>%
# filter(country_code %in% c("TUR", "ARM")) %>%
# filter(date == date_i) %>%
dplyr::left_join(
ds_geo %>% select(-country_name, -country_number),
by = c("country_code" )
) %>%
filter(
!is.na(country_label)
)
# ds0 %>% glimpse()Q0 - Response and Toll
What does Response to and Toll from COVID look for all OECD countries?
Response
# Why 75 days after exodus should be the starting point?
# 1. Most countries have peaked in their response
d1 <- ds0 %>% filter(oecd)
g1 <- ds0 %>%
# filter(country_code == "ITA") %>%
ggplot(aes(x = days_since_exodus, y = StringencyIndex, group = country_label, color = oecd))+
geom_line( alpha = .2)+
scale_color_manual(values = c("TRUE" = "darkorchid3", "FALSE" = "grey50"))+
geom_point(data = d1 %>% filter(days_since_1case == 1), size = 2, fill = "#1b9e77", color = "black", alpha = .5, shape = 21)+
geom_point(data = d1 %>% filter(days_since_1death == 1), size = 2, fill = "#d95f02", color = "black", alpha = .5, shape = 21)+
scale_x_continuous(breaks = seq(0,100, 25))+
labs(
title = "Timeline of countries' respones to COVID-19 as measured by the Stringency Index"
,y = "Stringency Index", x = "Days since first case outside of China (Jan 13, 2020)"
)+
geom_vline(xintercept = 58, linetype = "dotted")+
geom_vline(xintercept = 75, linetype = "dashed")+
geom_vline(xintercept = 100, linetype = "dashed", color = "red")
margings_for_plotly <- list(
l = 50,
r = 50,
b = 100,
t = 100,
pad = 4
)
g1 <- plotly::ggplotly(g1)
g1 %>% plotly::layout(autosize = F, width = 900, height = 600, margin = margings_for_plotly).
.
.
.
.
Toll
# 2. This is when the mortality curves starts going up
d2 <- ds0 %>% filter(oecd)
g2 <- ds0 %>%
# filter(country_code %in% ds_country$id) %>%
# filter(country_code == "ITA") %>%
ggplot(aes(x = days_since_exodus, y = n_deaths_cum_per_1m, group = country_label, color = oecd))+
geom_line( alpha = .2)+
scale_color_manual(values = c("TRUE" = "darkorchid3", "FALSE" = "grey50"))+
geom_point(data = d2 %>% filter(days_since_1case == 1), size = 2, fill = "#1b9e77", color = "black", alpha = .5, shape = 21)+
geom_point(data = d2 %>% filter(days_since_1death == 1), size = 2, fill = "#d95f02", color = "black", alpha = .5, shape = 21)+
scale_x_continuous(breaks = seq(0,100, 25))+
labs(
title = "Timeline of COVID-19 deaths per 1 million"
,y = "Total Deaths per 1 million", x = "Days since first case outside of China (Jan 13, 2020)"
)+
geom_vline(xintercept = 58, linetype = "dotted")+
geom_vline(xintercept = 75, linetype = "dashed")+
geom_vline(xintercept = 100, linetype = "dashed", color = "red")
margings_for_plotly <- list(
l = 50,
r = 50,
b = 100,
t = 100,
pad = 4
)
g2 <- plotly::ggplotly(g2)
g2 %>% plotly::layout(autosize = F, width = 900, height = 600, margin = margings_for_plotly)# g2 %>% plotly::layout(autosize = T).
.
.
.
.
Toll centered
# 3. Repositioning to the first death:
d3 <- ds0 %>% filter(oecd)
g3 <- ds0 %>%
# filter(country_code == "ITA") %>%
ggplot(aes(x = days_since_exodus, y = n_deaths_cum_per_1m, group = country_label, color = oecd))+
geom_line( alpha = .2)+
scale_color_manual(values = c("TRUE" = "darkorchid3", "FALSE" = "grey50"))+
geom_point(data = d3 %>% filter(days_since_1case == 1), size = 2, fill = "#1b9e77", color = "black", alpha = .5, shape = 21)+
geom_point(data = d3 %>% filter(days_since_1death == 1), size = 2, fill = "#d95f02", color = "black", alpha = .5, shape = 21)+
scale_x_continuous(breaks = seq(-100,100, 25))+
labs(
title = "Timeline of COVID-19 deaths per 1 million (centered)"
,y = "Total Deaths (per 1 million)", x = "Days since first confirmed death in the country"
)
margings_for_plotly <- list(
l = 50,
r = 50,
b = 100,
t = 100,
pad = 4
)
g3 <- plotly::ggplotly(g3)
g3 %>% plotly::layout(autosize = F, width = 900, height = 600, margin = margings_for_plotly)# g3 %>% plotly::layout(autosize = T).
.
.
.
.
Q1 - Shape of the trend
How does COVID-19 progress in each country?
# ds0 %>% glimpse()
d1 <- ds0 %>% filter(oecd) %>%
mutate(
n_cases_cum = n_cases_cum / 1000
,n_cases_cum_per_1m = n_cases_cum_per_1m / 1000
)
g1 <- d1 %>%
ggplot(aes(
x = days_since_exodus
,y = n_cases_cum
# ,y =n_cases_cum_per_1m
# ,y = n_deaths_cum
# ,y = n_deaths_cum_per_1m
))+
geom_line(size = .5)+
# geom_line(aes(y=StringencyIndex), color = "red")+
facet_wrap(~country_label, scale = "free", ncol = 6)+
geom_point(
data = d1 %>% filter(days_since_1case == 1)
,size = 2, fill = "#1b9e77", color = "black", alpha = .5, shape = 21
)+
geom_point(
data = d1 %>% filter(days_since_1death == 1)
,size = 2, fill = "#d95f02", color = "black", alpha = .5, shape = 21
)+
geom_vline(xintercept = 58, linetype = "dotted",)+
geom_vline(xintercept = 75, linetype = "dashed", alpha = .5)+
geom_vline(xintercept = 100, linetype = "dashed", color = "red", alpha = .5)+
scale_x_continuous(breaks = seq(0,100, 50))+
labs(
title = "Timeline of COVID-19: Cumulative Cases"
, y = "Cumulative Cases (in thousands)", x = "Days since first case outside of China (Jan 13, 2020)"
, caption = "(first dot) = 1st confirmed case, (second dot) = 1st confirmed death,
(dotted line) = pandemic announced by WHO, (dashed lines) = 75 and 100th day since Exodus"
)
cat("\n## Cases\n")Cases
g1
cat("\n## Cases per 1m\n")Cases per 1m
g1 + aes(y = n_cases_cum_per_1m)+labs(y = "Cumulative Cases per 1 mil (in thousands)",
title = "Timeline of COVID-19: Cumulative Cases per 1 million")
cat("\n## Deaths\n")Deaths
g1 + aes(y = n_deaths_cum)+labs(y = "Cumulative Deaths",
title = "Timeline of COVID-19: Cumulative Deaths")
cat("\n## Deaths per 1m\n")Deaths per 1m
g1 + aes(y = n_deaths_cum_per_1m)+labs(y = "Cumulative Deaths per 1 mil",
title = "Timeline of COVID-19: Cumulative Deaths per 1 million")
Q1 - Within Country
How do key indices compare within each country?
Common legend:
- (first dot) = 1st confirmed case
- (second dot) = 1st confirmed death
- (dotted line) = pandemic announced by WHO (March 11)
- (dashed lines) = 75th and 100th day since Exodus (January 13), first confirmed case outside China
- X-asis measures
Days since Exodus
# Q How do key indices compare within each country?
focal_vars <- c( "n_cases_cum", "n_cases_cum_per_1m", "n_deaths_cum", "n_deaths_cum_per_1m",
"StringencyIndex")
ds1 <- ds0 %>% filter(oecd) %>%
# dplyr::filter(country_code %in% c("ITA","FRA")) %>%
dplyr::select(country_code, country_label, days_since_exodus, days_since_1case,
days_since_1death,
n_cases_cum, n_cases_cum_per_1m, n_deaths_cum, n_deaths_cum_per_1m,
StringencyIndex
) %>%
tidyr::pivot_longer(cols = focal_vars, values_to = "value", names_to = "metric")
print_one_wrap <- function(d, country = "ITA"){
# d <- ds1; country = "ITA"
d1 <- d %>% filter(country_code == country) %>%
dplyr::mutate(
country_label = stringr::str_replace_all(country_label,"\\(the Republic of\\)","")
)
g1 <- d1 %>%
ggplot(aes(x = days_since_exodus, y = value))+
geom_line()+
geom_point(
data = d1 %>% filter(days_since_1case == 1),
size = 2, fill = "#1b9e77", color = "black", alpha = .5, shape = 21)+
geom_point(
data = d1 %>% filter(days_since_1death == 1),
size = 2, fill = "#d95f02", color = "black", alpha = .5, shape = 21)+
geom_vline(xintercept = 58, linetype = "dotted",)+
geom_vline(xintercept = 75, linetype = "dashed", alpha = .5)+
geom_vline(xintercept = 100, linetype = "dashed", color = "red", alpha = .5)+
facet_wrap(country_label ~ metric, scale = "free_y",ncol = 5,
labeller = labeller(metric = c(
"n_cases_cum" = " Cases",
"n_cases_cum_per_1m" = " Cases per million",
"n_deaths_cum" = "Deaths",
"n_deaths_cum_per_1m" = "Deaths per million",
"StringencyIndex" = "Stringency Index"
)))+
labs(y = NULL, x = NULL)
g1
}
# ds1 %>% print_one_wrap(country = 'ITA')
countries <- unique(ds1$country_code)
for(country_i in countries){
# cat("\n#### ", country_i,"\n")
ds1 %>% print_one_wrap(country = country_i) %>% print()
cat("\n")
}
Q2 - Response
What was the trend of the response to COVID-10 by each country?
# What the trend response to COVID-10 by each country?
d1 <- ds0 %>% filter(oecd)
g1 <- d1 %>%
ggplot(aes(x = days_since_exodus, y = StringencyIndex))+
geom_line()+
geom_point(data = d1 %>% filter(days_since_1case == 1), size = 2, fill = "#1b9e77", color = "black", alpha = .5, shape = 21)+
geom_point(data = d1 %>% filter(days_since_1death == 1), size = 2, fill = "#d95f02", color = "black", alpha = .5, shape = 21)+
facet_wrap(~country_label)+
geom_vline(xintercept = 58, linetype = "dotted",)+
geom_vline(xintercept = 75, linetype = "dashed", alpha = .5)+
geom_vline(xintercept = 100, linetype = "dashed", color = "red", alpha = .5)+
labs(
title = "Timeline of OECD countries' respones to COVID-19 as measured by the Stringency Index"
,y = "Stringency Index", x = "Days since first case outside of China (Jan 13, 2020)"
, caption = "(first dot) = 1st confirmed case, (second dot) = 1st confirmed death,
(dotted line) = pandemic announced by WHO, (dashed lines) = 75 and 100th day since Exodus"
)
cat("\n## Stringency\n")Stringency
g1
cat("\n")cat("\n## Government Response\n")Government Response
g2 <- g1+aes(y = GovernmentResponseIndex)+labs(
title = "Timeline of OECD countries' respones to COVID-19 as measured by the Government Response Index", y="Gov Response Index")
g2
cat("\n")cat("\n## Containment\n")Containment
g3 <- g1+aes(y = ContainmentHealthIndex)+labs(
title = "Timeline of OECD countries' respones to COVID-19 as measured by the Containment Index", y="Containment Index")
g3
cat("\n")cat("\n## Economic Support\n")Economic Support
g4 <- g1+aes(y = EconomicSupportIndex )+labs(
title = "Timeline of OECD countries' respones to COVID-19 as measured by the Economic Support Index", y="Econ Support Index")
g4
session information
For the sake of documentation and reproducibility, the current report was rendered in the following environment. Click the line below to expand.
Environment
- Session info -------------------------------------------------------------------------------------------------------
setting value
version R version 3.6.3 (2020-02-29)
os Windows 10 x64
system x86_64, mingw32
ui RStudio
language (EN)
collate English_United States.1252
ctype English_United States.1252
tz America/New_York
date 2020-05-29
- Packages -----------------------------------------------------------------------------------------------------------
package * version date lib source
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backports 1.1.5 2019-10-02 [1] CRAN (R 3.6.1)
callr 3.4.3 2020-03-28 [1] CRAN (R 3.6.3)
cli 2.0.2 2020-02-28 [1] CRAN (R 3.6.3)
codetools 0.2-16 2018-12-24 [2] CRAN (R 3.6.3)
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[1] C:/Users/an499583/Documents/R/win-library/3.6
[2] C:/Users/an499583/Documents/R/R-3.6.3/library